The Transferable Belief Model For Failure Prediction In Wireless Sensor Networks

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A Low-Resources Hardware-based Audio Data Compression Scheme for Wireless Sensors Networks

Over the last two decades, the Wireless Multimedia Sensors Networks (WMSN) technology have become increasingly popular by both actual industrial users and research community, they are used for recording speech and then sending it to a base station. However, their limited amount of resources (power, low capacity of radio waves, bandwidth, memory, processing, storage, etc.) makes it important to save resources in order to extend the life of the sensor as long as possible. This paper aims to propose and evaluate an adaptive lifting wavelet encoding hardware solution for audio data compression in WMSN, with require low memory, low computation and low energy consumption. The simulation results show that the proposed approach is efficient and satisfactory compared to the Discrete Cosine Transform (DCT) approach, since it allows 32.6% storage savings and 47.84% energy savings were achieved

The Transferable Belief Model for Failure Prediction in Wireless Sensor Networks

International audienceIn recent years, with the advent of smart sensors, the design and deployment of Wireless Sensor Networks (WSN) have become an active area of research. Sensor nodes are typically deployed to sense, measure and gather parameters/conditions such as temperature, pressure, humidity, vibration, etc. Then, the Industrial Internet of Things (IIoT) applies WSN to improve the productivity and efficiency of existing and prospective manufacturing industries. The measured values are transmitted to a data fusion centre for further processing. However, the set of learning data on which the processing must be based for decision making is not always complete due to some failures such as security attack, packet leak, collision, interference, congestion, channel fading, devices errors, incorrect deployment, synchronization issues, environmental blockages or unknown errors. The management of missing data is a common and widespread problem in smart factory using WSN. It is a more common topic in monitoring systems. The matter is to define a Transferable Belief Model (TBM) for failures prediction in WSN. However, most of the existing prediction methods are based on deterministic probabilistic approaches. These approaches are not adapted for representation of imprecise knowledge common in WSN. To remedy these issues, the ultimate goal of this paper is to propose a non-deterministic approach for building a predictive belief function from statistical data for decision making. The fundamental point of methodology is to transfer uncertainties by transforming masses function into densities function, and then belief functions, plausibility functions and commonality functions into integrals of these densities function. The efficiency of the approach is demonstrated using a simulated WSN problem and Monte Carlo simulation. The simulation’s results have shown that the proposed approach achieves satisfactory performance compared to data imputation methods

DDCA-WSN: A Distributed Data Compression and Aggregation Approach for Low Resources Wireless Sensors Networks

International audienceWireless Sensor Networks (WSN) have been as useful and beneficial as resource-constrained distributed event-based system for several scenarios. Yet, in WSN, optimisation of limited resources (energy, computing memory, bandwidth and storage) during data collection and communication process is a major challenge. Data redundancy involves a large consumption of sensor resources during processing and transferring information to an analysis centre. As a matter of fact, most of energy consumption (as much as 80%) for standard WSN applications lies in the radio module where receiving and sending packets is necessary to communicate between stations. Thus, this paper proposes an approach to achieve optimal sensor resources by data compression and aggregation regarding integrity of raw data. Then, the main objective is to reduce this redundancy by discarding a certain number of packets of information and keeping only the most meaningful and informative ones for the reconstruction. Data aggregation discarded a certain sensing data packet, which lead to low data-rate communication and low likelihood of packet collisions on the wireless medium. Data compression reduces a redundancy in keeping aggregated data, in order to diminish resources consumption of wireless sensor nodes, which leads to storage saving and sending only a small data stream in the bandwidth of communication. The performances of the proposed approach DDCA-WSN are qualified using experimental simulation on OMNeT++/Castalia. The performance metrics were evaluated in terms of Compression Ratio (CR), data Aggregation Rate (AR), Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE) and Energy Consumption (EC).The obtained results have significantly increased the network lifetime. Moreover, the integrity (quality) of the raw data is guaranteed

The Transferable Belief Model for Network Performance Reliability Analysis in Wireless Sensor Networks

International audienceIn view of the prospects offered by the Wireless Sensor Networks (WSNs), some of the main challenges in recent years concern access to more reliable information (event detection) for an efficient decision making. Moreover, WSNs are one of the most speedily developing information technologies and promise to have a variety of applications in Next Generation Networks (NGN), Internet of Things (IoT), and for critical and safety relevant applications.Reliability is one of the most important attribute of such systems. Thus, in the field of artificial intelligence, the development of intelligent systems for processing imprecise and/or uncertain information is proposed. Truth be told, reliable event detection at the sink node depends on collective information given by source nodes and not just on any individual report. Subsequently, the WSNs world view requires a collective event to the sink reliability notion instead of the conventional end to end notion. This paper analyses the reliability of WSNs by adopting the theory of belief functions. The proposed Transferable Belief Model (TBM) approach aims to analyse reliability in WSNs according to the network topology. It includes a reliability of coverage, reliability of packet delivery, reliability of secure data exchange, reliability of network availability and reliability in terms of network latency

A Lossless Distributed Data Compression and Aggregation Methods for Low Resources Wireless Sensors Platforms

International audienceWireless Multimedia Sensor Networks (WMSN) are undoubtedly one of the technologies that will transform the world around all of us. Still, they have been as useful and beneficial as resource-constrained distributed event-based system for several scenarios. Yet, in WMSN, optimisation of limited resources (energy, computing memory, bandwidth, storage and so on) during data collection, processing and communication process is a major challenge to guarantee the high performance of the system. Unfortunately, data redundancy involves a large consumption of sensor resources during processing and transferring information to an analysis centre. As a matter of fact, most of energy consumption (as much as 80%) for standard WSN applications lies in the radio module where receiving and sending packets is necessary to communicate between stations. To tackle this issue, this paper proposes an approach to achieve optimal sensor resources by data compression and aggregation regarding integrity of raw data. Then, the main objective is to reduce this redundancy by discarding a certain number of packets of information and keeping only the most meaningful and informative ones for the reconstruction. Data aggregation discarded a certain sensing data packet, which lead to low data-rate communication and low likelihood of packet collisions on the wireless medium. Data compression reduces a redundancy in keeping aggregated data, which leads to storage saving and sending only a small data stream in the bandwidth of communication. The performances of the proposed approach are qualified using experimental simulation on OMNeT +  + /Castalia. The performance metrics were evaluated in terms of data Aggregation Rate (AR), Compression Ratio (CR), Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Energy Consumption (EC).The obtained results have significantly increased the life span of the sensors and thus the lifetime of the network. Furthermore, the integrity (quality) of the raw data is guaranteed